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      Department of Systems Engineering and Engineering Management



                         The Chinese University of Hong Kong



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Date: Monday, April 28 4:00 pm – 5:00 pm



Venue: ERB 513, The Chinese University of Hong Kong



Title: Graph Machine Learning: Foundations and Perspectives



Speaker: Professor Zhewei Wei, Renmin University of China



Abstract:  In recent years, research on analyzing and mining graph

data using machine learning methods has received increasing attention

due to the significant expressive power of graph-structured data.

Graph Neural Networks (GNNs), a family of deep learning-based methods

for processing graph data, have shown excellent performance in many

fields and have become widely used for graph analysis. In this talk,

we will explore key tasks and cutting-edge applications of GNNs. We

will then present three of our recent research works on the

scalability of spectral GNNs, temporal graph learning, and graph for

science. Finally, we will talk about future perspectives for graph

machine learning.



Speaker Bio: Zhewei Wei is a Professor at the Gaoling School of

Artificial Intelligence, Renmin University of China. He received his

BSc from Peking University in 2008 and his Ph.D. from Hong Kong

University of Science and Technology in 2012. Afterward, he worked as

a Postdoc at Aarhus University, from 2012 to 2014, and then joined

Renmin University of China in 2014. He has published over 80 papers in

top conferences and journals (e.g., STOC, SIGMOD, ICML, KDD) in the

fields of theoretical computer science, databases, machine learning,

and data mining. He received the Alberto Mendelzon PODS 2022 Test of

Time Award, the 2023 World Artificial Intelligence Conference Youth

Outstanding Paper Nomination Award, and the VLDB 2023 Best Paper

Nomination Award. He has served as an Associate Editor for IEEE TPAMI,

the Proceedings Chair for PODS and ICDT, and the Area Chair for ICML,

NeurIPS, and ICLR. His Ph.D. students have been awarded the Baidu

Scholarship 2021 (10 worldwide), the Microsoft Scholar 2022 (12 in the

Asia-Pacific region), and the CCF Outstanding Doctorial Dissertation

Award (10 nationwide).